Search and RBS Notes
Rule Based Systems and Search Notes
1. Rule-Based Systems:
General Forward Chaining Pseudo code
1. For all rules, and assertions, find all matches, i.e. Rule+Assertion combinations.
2. Check if any of the matches are defunct.
A defunc
History of AI
Image source
Origins of AI
1940s
1950
McCulloch & Pitts neurons; Hebbs learning rule
Turings Computing Machinery and Intelligence
Shannons computer chess
1954
Georgetown-IBM machine translation experiment
1956
Dartmouth meeting: Artificial I
Game theory
Game theory deals with systems of interacting
agents where the outcome for an agent depends
on the actions of all the other agents
Applied in sociology, politics, economics, biology, and,
of course, AI
Agent design: determining the best str
Review: Game theory
Alice:
Testify
Alice:
Refuse
Bob:
Testify
-5,-5
-10,0
Bob:
Refuse
0,-10
-1,-1
Dominant strategy
Nash equilibrium
Pareto optimal outcome
Game of Chicken
Player 1
Player 2
Straight
Chicken
Chicken
Straight
S
C
S -10, -10
-1, 1
C
0, 0
Rational Agents (Chapter 2)
Outline
Agent function and agent program
Rationality
PEAS (Performance measure, Environment, Actuators,
Sensors)
Environment types
Agent types
Agents
An agent is anything that can be viewed as perceiving its
environment t
Logic
Knowledge-based agents
Inference engine
Domain-independent algorithms
Knowledge base
Domain-specific content
Knowledge base (KB) = set of sentences in a formal language
Declarative approach to building an agent (or other system):
Tell it what it
Solving problems by searching
Chapter 3
Search
We will consider the problem of designing goalbased agents in fully observable, deterministic,
discrete, known environments
Example:
Start state
Goal state
Search
We will consider the problem of designing
COMP 590: Artificial Intelligence
Today
Course overview
What is AI?
Examples of AI today
Who is this course for?
An introductory survey of AI techniques for
students who have not previously had an
exposure to this subject
Juniors, seniors, beginning
Local search algorithms
Some types of search problems can be formulated
in terms of optimization
We dont have a start state, dont care about the path
to a solution
We have an objective function that tells us about
the quality of a possible solution, an
Constraint Satisfaction Problems
Constraint satisfaction problems (CSPs)
Definition:
State is defined by variables Xi with values from domain Di
Goal test is a set of constraints specifying allowable
combinations of values for subsets of variables
Sol
Review: Search problem formulation
Initial state
Actions
Transition model
Goal state
Path cost
What is the optimal solution?
What is the state space?
Review: Tree search
Initialize the fringe using the starting state
While the fringe is not empty
Cho
COMP 590-096 Fall 2010
Midterm Review
Terms
Be able to define the following terms and answer basic questions about them:
Agents and environments
o Rationality
o PEAS
o Environment characteristics: fully vs. partially observable, deterministic vs.
stochast
Decision making in episodic
environments
We have just looked at decision making in
sequential environments
Now lets consider the easier problem of
episodic environments
The agent gets a series of unrelated problem
instances and has to make some decisio
First-Order Logic
Limitations of propositional logic
Suppose you want to say All humans are mortal
In propositional logic, you would need
~6.7 billion statements
Suppose you want to say Some people can run a
marathon
You would need a disjunction of ~6
Inference in FOL
All rules of inference for propositional logic apply
to first-order logic
We just need to reduce FOL sentences to PL
sentences by instantiating variables and
removing quantifiers
Reduction of FOL to PL
Suppose the KB contains the follo
Probability
Uncertainty
Let action At = leave for airport t minutes before flight
Will At get me there on time?
Problems:
Partial observability (road state, other drivers' plans, etc.)
Noisy sensors (traffic reports)
Uncertainty in action outcomes (fla
Chapter 1:
Introduction to
Expert Systems
Expert Systems: Principles and
Programming, Fourth Edition
Objectives
Learn the meaning of an expert system
Understand the problem domain and knowledge
domain
Learn the advantages of an expert system
Understan
Algorithmic Trading of Futures via
Machine Learning
David Montague, [email protected]
lgorithmic trading of securities has become
a staple of modern approaches to financial
investment. In this project, I attempt to
obtain an effective strategy for trad
Lab 4
To work on this problem set, you will need to get the code:
This lab has two parts; the first part is on CSPs and the second part is on learning algorithms,
specifically KNN and decision trees.
Constraint Satisfaction Problems
In this portion of Lab
Lab 3
To work on this problem set, you will need to get the code, much like you did for earlier problem sets.
Your answers for the problem set belong in the main file lab3.py.
Game search
This problem set is about game search, and it will focus on the gam
Games and CSP Notes
Games and CSP search
1. Games:
A) General Minimax search:
function max-value(state, depth)
1. if state is an end-state (end of game) or depth is 0
return SV(state)
2. v = -infinity
3. for s in get-all-next-moves(state)
v = max(v, min-v
Lab 5
This is the last problem set in 6.034! To work on this problem set, you will need to get the code.
You will need to download and install an additional software package called Orange for the second
part of the lab. Please download Orange first so tha
Top Down Approach To Neural Nets
MAJOR HINT for Neural Nets portion of Lab 5
Figure 1: A two layer Neural Network
To understand what to implement for dOutDx(self, weight) for lab 5, you'll need to
thoroughly understand how the error update equation for ba
KNN-ID and Neural Nets
KNN, ID Trees, and Neural Nets
Intro to Learning Algorithms
KNN, Decision trees, Neural Nets are all supervised learning algorithms
Their general goal = make accurate predictions about unknown data after being trained on known
data.
Probability
Probability, Bayes Nets, Naive Bayes, Model Selection
Major
1.
2.
3.
4.
5.
6.
7.
8.
Ideas:
Intro to Bayes nets: what they are and what they represent.
How to compute the joint probability from the Bayes net.
How to compute the conditional prob
Lab 1
To work on this problem set, you will need to get the code, much like you did for Lab 0.
Most of your answers belong in the main file lab1.py. However, the more involved coding problems
in section 2 have their own separate files.
You will probably w
SVM and Boosting
Support Vector Machines
In SVMs we are trying to find a decision boundary that maximizes the "margin" or the "width of
the road" separating the positives from the negative training data points.
To find this we minimize:
subject to the con
Lab 0
The purpose of this lab is to familiarize you with this term's lab system and to diagnose your
programming ability and facility with Python. 6.034 uses Python for all of its labs, and you will be
called on to understand the functioning of large syst